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Sample Size Calculation for Differential Expression Analysis of RNA-Seq Data

In: Frontiers of Biostatistical Methods and Applications in Clinical Oncology

Author

Listed:
  • Stephanie Page Hoskins

    (Vanderbilt University, Center for Quantitative Sciences)

  • Derek Shyr

    (Harvard T.H. Chan School of Public Health, Department of Biostatistics)

  • Yu Shyr

    (Vanderbilt University, Center for Quantitative Sciences)

Abstract

The Holy Grail of precision medicine is the comprehensive integration of patient genotypic with phenotypic data to develop personalized disease prevention and treatment strategies. Next-generation sequencing technologies (NGS) and other types of high-throughput assays have exploded in popularity in recent years, thanks to their ability to produce an enormous volume of data quickly and at relatively low cost compared to more traditional laboratory methods. The ability to generate big data brings us one step closer to the realization of precision medicine; nevertheless, across the life cycle of such data, from experimental design to data capture, management, analysis, and utilization, many challenges remain. In this paper, we reviewed and discussed several statistical methods to estimate sample size based on the Poisson and Negative Binomial distributions for RNAseq experimental design.

Suggested Citation

  • Stephanie Page Hoskins & Derek Shyr & Yu Shyr, 2017. "Sample Size Calculation for Differential Expression Analysis of RNA-Seq Data," Springer Books, in: Shigeyuki Matsui & John Crowley (ed.), Frontiers of Biostatistical Methods and Applications in Clinical Oncology, pages 359-379, Springer.
    • Handle: RePEc:spr:sprchp:978-981-10-0126-0_22
    DOI: 10.1007/978-981-10-0126-0_22
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